/**
 * Copyright (c) 2025 Huawei Technologies Co., Ltd.
 * This program is free software, you can redistribute it and/or modify it under the terms and conditions of 
 * CANN Open Software License Agreement Version 2.0 (the "License").
 * Please refer to the License for details. You may not use this file except in compliance with the License.
 * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, 
 * INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
 * See LICENSE in the root of the software repository for the full text of the License.
*/
/*!
 * \file test_fused_cross_entropy_loss_with_max_sum.cpp
 * \brief
 */
#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_fused_cross_entropy_loss_with_max_sum.h"

#define CHECK_RET(cond, return_expr) \
  do {                               \
    if (!(cond)) {                   \
      return_expr;                   \
    }                                \
  } while (0)

#define LOG_PRINT(message, ...)     \
  do {                              \
    printf(message, ##__VA_ARGS__); \
  } while (0)

int64_t GetShapeSize(const std::vector<int64_t>& shape) {
  int64_t shapeSize = 1;
  for (auto i : shape) {
    shapeSize *= i;
  }
  return shapeSize;
}

int Init(int32_t deviceId, aclrtStream* stream) {
  // 固定写法，资源初始化
  auto ret = aclInit(nullptr);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclInit failed. ERROR: %d\n", ret); return ret);
  ret = aclrtSetDevice(deviceId);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetDevice failed. ERROR: %d\n", ret); return ret);
  ret = aclrtCreateStream(stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateStream failed. ERROR: %d\n", ret); return ret);
  return 0;
}

template <typename T>
int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr, aclDataType dataType, aclTensor** tensor) {
  auto size = GetShapeSize(shape) * sizeof(T);
  // 调用aclrtMalloc申请device侧内存
  auto ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret); return ret);
  // 调用aclrtMemcpy将host侧数据拷贝到device侧内存上
  ret = aclrtMemcpy(*deviceAddr, size, hostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret); return ret);

  // 计算连续tensor的strides
  std::vector<int64_t> strides(shape.size(), 1);
  for (int64_t i = shape.size() - 2; i >= 0; i--) {
    strides[i] = shape[i + 1] * strides[i + 1];
  }

  // 调用aclCreateTensor接口创建aclTensor
  *tensor = aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND,
                            shape.data(), shape.size(), *deviceAddr);
  return 0;
}

int main() {
  // 1. （固定写法）device/stream初始化，参考acl API手册
  // 根据自己的实际device填写deviceId
  int32_t deviceId = 0;
  aclrtStream stream;
  auto ret = Init(deviceId, &stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);

  // 2. 构造输入与输出，需要根据API的接口自定义构造
  std::vector<int64_t> logitsMaxShape = {2};
  std::vector<int64_t> sumExpLogitsShape = {2};
  std::vector<int64_t> predictedLogitsShape = {2};
  std::vector<int64_t> inputShape = {2};
  std::vector<int64_t> weightShape = {2};
  std::vector<int64_t> vocabParallelLogitsOptionalShape = {2, 2};
  std::vector<int64_t> lossOutShape = {2};
  std::vector<int64_t> softMaxOutOptionalShape = {2, 2};

  float labelSmoothing = 0;

  void* logitsMaxDeviceAddr = nullptr;
  void* sumExpLogitsDeviceAddr = nullptr;
  void* predictedLogitsDeviceAddr = nullptr;
  void* inputDeviceAddr = nullptr;
  void* weightDeviceAddr = nullptr;
  void* vocabParallelLogitsOptionalDeviceAddr = nullptr;
  void* lossOutDeviceAddr = nullptr;
  void* softMaxOutOptionalDeviceAddr = nullptr;

  aclTensor* logitsMax = nullptr;
  aclTensor* sumExpLogits = nullptr;
  aclTensor* predictedLogits = nullptr;
  aclTensor* input = nullptr;
  aclTensor* weight = nullptr;
  aclTensor* vocabParallelLogitsOptional = nullptr;
  aclTensor* lossOut = nullptr;
  aclTensor* softMaxOutOptional = nullptr;

  std::vector<float> logitsMaxHostData = {0.5, 1};
  std::vector<float> sumExpLogitsHostData = {0.5, 1};
  std::vector<float> predictedLogitsHostData = {0.5, 1};
  std::vector<float> inputHostData = {0, 1};
  std::vector<float> weightHostData = {0, 1};
  std::vector<float> vocabParallelLogitsOptionalHostData = {1, 0.5, 0.5, 1};
  std::vector<float> lossOutHostData = {0, 0};
  std::vector<float> softMaxOutOptionalHostData = {0, 0, 0, 0};
  // 创建 aclTensor
  ret = CreateAclTensor(logitsMaxHostData, logitsMaxShape, &logitsMaxDeviceAddr, aclDataType::ACL_FLOAT, &logitsMax);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  ret = CreateAclTensor(sumExpLogitsHostData, sumExpLogitsShape, &sumExpLogitsDeviceAddr, aclDataType::ACL_FLOAT, &sumExpLogits);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  ret = CreateAclTensor(predictedLogitsHostData, predictedLogitsShape, &predictedLogitsDeviceAddr, aclDataType::ACL_FLOAT, &predictedLogits);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  ret = CreateAclTensor(inputHostData, inputShape, &inputDeviceAddr, aclDataType::ACL_FLOAT, &input);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  ret = CreateAclTensor(weightHostData, weightShape, &weightDeviceAddr, aclDataType::ACL_FLOAT, &weight);
  CHECK_RET(ret == ACL_SUCCESS, return ret);


  ret = CreateAclTensor(vocabParallelLogitsOptionalHostData, vocabParallelLogitsOptionalShape, &vocabParallelLogitsOptionalDeviceAddr, aclDataType::ACL_FLOAT, &vocabParallelLogitsOptional);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  ret = CreateAclTensor(lossOutHostData, lossOutShape, &lossOutDeviceAddr, aclDataType::ACL_FLOAT, &lossOut);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  ret = CreateAclTensor(softMaxOutOptionalHostData, softMaxOutOptionalShape, &softMaxOutOptionalDeviceAddr, aclDataType::ACL_FLOAT, &softMaxOutOptional);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3. 调用CANN算子库API，需要修改为具体的Api名称
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // 调用aclnnFusedCrossEntropyLossWithMaxSum第一段接口
  ret = aclnnFusedCrossEntropyLossWithMaxSumGetWorkspaceSize(logitsMax, sumExpLogits, predictedLogits, labelSmoothing, input, weight, 
                                                          vocabParallelLogitsOptional, lossOut, softMaxOutOptional, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnFusedCrossEntropyLossWithMaxSumGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // 根据第一段接口计算出的workspaceSize申请device内存
  void* workspaceAddr = nullptr;
  if (workspaceSize > 0) {
    ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret);
  }
  // 调用aclnnFusedCrossEntropyLossWithMaxSum第二段接口
  ret = aclnnFusedCrossEntropyLossWithMaxSum(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnFusedCrossEntropyLossWithMaxSum failed. ERROR: %d\n", ret); return ret);

  // 4. （固定写法）同步等待任务执行结束
  ret = aclrtSynchronizeStream(stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

  // 5. 获取输出的值，将device侧内存上的结果拷贝至host侧，需要根据具体API的接口定义修改
  auto size = GetShapeSize(lossOutShape);
  std::vector<float> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), lossOutDeviceAddr,
                    size * sizeof(resultData[0]), ACL_MEMCPY_DEVICE_TO_HOST);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy result from device to host failed. ERROR: %d\n", ret); return ret);
  for (int64_t i = 0; i < size; i++) {
    LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);
  }

  size = GetShapeSize(softMaxOutOptionalShape);
  std::vector<float> secondResultData(size, 0);
  ret = aclrtMemcpy(secondResultData.data(), secondResultData.size() * sizeof(secondResultData[0]), softMaxOutOptionalDeviceAddr,
                    size * sizeof(secondResultData[0]), ACL_MEMCPY_DEVICE_TO_HOST);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy result from device to host failed. ERROR: %d\n", ret); return ret);
  for (int64_t i = 0; i < size; i++) {
    LOG_PRINT("result[%ld] is: %f\n", i, secondResultData[i]);
  }

  // 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
  aclDestroyTensor(logitsMax);
  aclDestroyTensor(sumExpLogits);
  aclDestroyTensor(predictedLogits);
  aclDestroyTensor(input);
  aclDestroyTensor(weight);
  aclDestroyTensor(vocabParallelLogitsOptional);
  aclDestroyTensor(lossOut);
  aclDestroyTensor(softMaxOutOptional);

  // 7. 释放device资源，需要根据具体API的接口定义修改
  aclrtFree(logitsMaxDeviceAddr);
  aclrtFree(sumExpLogitsDeviceAddr);
  aclrtFree(predictedLogitsDeviceAddr);
  aclrtFree(inputDeviceAddr);
  aclrtFree(weightDeviceAddr);
  aclrtFree(vocabParallelLogitsOptionalDeviceAddr);
  aclrtFree(lossOutDeviceAddr);
  aclrtFree(softMaxOutOptionalDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
}